Browsing by Author "Jadhav, Swati"
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Item Open Access Data mining in computational finance(2017-12) Jadhav, Swati; Jenkins, Karl W.; He, HongmeiComputational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset.Item Open Access Information gain directed genetic algorithm wrapper feature selection for credit rating(Elsevier, 2018-04-22) Jadhav, Swati; He, Hongmei; Jenkins, Karl W.Financial credit scoring is one of the most crucial processes in the finance industry sector to be able to assess the credit-worthiness of individuals and enterprises. Various statistics-based machine learning techniques have been employed for this task. “Curse of Dimensionality” is still a significant challenge in machine learning techniques. Some research has been carried out on Feature Selection (FS) using genetic algorithm as wrapper to improve the performance of credit scoring models. However, the challenge lies in finding an overall best method in credit scoring problems and improving the time-consuming process of feature selection. In this study, the credit scoring problem is investigated through feature selection to improve classification performance. This work proposes a novel approach to feature selection in credit scoring applications, called as Information Gain Directed Feature Selection algorithm (IGDFS), which performs the ranking of features based on information gain, propagates the top m features through the GA wrapper (GAW) algorithm using three classical machine learning algorithms of KNN, Naïve Bayes and Support Vector Machine (SVM) for credit scoring. The first stage of information gain guided feature selection can help reduce the computing complexity of GA wrapper, and the information gain of features selected with the IGDFS can indicate their importance to decision making.Item Open Access Prediction of earnings per share for industry(IEEE, 2016-08-01) Jadhav, Swati; He, Hongmei; Jenkins, Karl W.Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance.